T1 acquisition parameters.

The b-value in the diffusion magnetic resonance image(dMRI) reflects the degree to which the water molecules are affected by the magnetic field gradient pulse in the tissue, and the different b-values not only affect the image contrast but also the accuracy of the subsequent calculation. The imbalance between the lower and higher b-value image categories in the macaque dMRI brain imaging dataset dramatically affects the accuracy of computational neuroscience. The medical image conversion method based on the generative adversarial network can generate different b-value images. However, the macaque brain dataset has multi-center and small-sample problems, which restricts the training effect of the general model. To increase macaques’ lower b-value dMRI data, we propose a variable multi-modal image feature fusion adversarial neural network called RISNet. The network can use the proposed rapid insertion structural(RIS) to input features from different modes into a general residual decoding structure to enhance the model’s generalization ability. The RIS combines the advantages of multi-modal data, which can quickly rewrite the network and extract and fuse the features of multi-modal data. We used a T1 image and a higher b-value image of the brain as model inputs to generate high-quality, lower b-value images. Experimental results show that our method improves the PSNR index by 1.8211 on average and the SSIM index by 0.0111 compared with other methods. In addition, in terms of qualitative observation and DTI estimation, our process also shows sound visual effects and strong generalization ability. These advantages make our method an effective means to solve the problem of dMRI brain image conversion in macaques and provide strong support for future neuroscience research.

扩散磁共振成像（diffusion magnetic resonance imaging, dMRI）中的b值反映了水分子在组织中受磁场梯度脉冲影响的程度，不同的b值不仅会影响图像对比度，还会影响后续计算的精度。猕猴dMRI脑成像数据集内低b值与高b值图像类别分布失衡的问题，会严重影响计算神经科学研究的精度。基于生成对抗网络（generative adversarial network, GAN）的医学图像转换方法可生成不同b值的图像，但猕猴脑数据集存在多中心、小样本的问题，这限制了通用模型的训练效果。为扩充猕猴的低b值dMRI数据，我们提出了一种可变多模态图像特征融合对抗神经网络，命名为RISNet。该网络采用我们提出的快速插入结构（rapid insertion structural, RIS），可将不同模态的特征输入至通用残差解码结构中，以提升模型的泛化能力。RIS融合了多模态数据的优势，能够快速重构网络并提取、融合多模态数据的特征。我们以脑部的T1图像与高b值图像作为模型输入，生成高质量的低b值图像。实验结果表明，相较于其他方法，本方法的峰值信噪比（Peak Signal-to-Noise Ratio, PSNR）指标平均提升1.8211，结构相似性指数（Structural Similarity, SSIM）平均提升0.0111。此外，在定性观测与扩散张量成像（Diffusion Tensor Imaging, DTI）估计方面，本方法同样展现出优异的视觉效果与较强的泛化能力。这些优势使得本方法成为解决猕猴dMRI脑图像转换问题的有效手段，可为未来的神经科学研究提供有力支撑。